Self-Supervised Visual Tracking via Image Synthesis and Domain Adversarial Learning
With the widespread use of sensors in applications such as autonomous driving and intelligent security, stable and efficient target tracking from diverse sensor data has become increasingly important. Self-supervised visual tracking has attracted increasing attention due to its potential to eliminat...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/15/4621 |
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| author | Gu Geng Sida Zhou Jianing Tang Xinming Zhang Qiao Liu Di Yuan |
| author_facet | Gu Geng Sida Zhou Jianing Tang Xinming Zhang Qiao Liu Di Yuan |
| author_sort | Gu Geng |
| collection | DOAJ |
| description | With the widespread use of sensors in applications such as autonomous driving and intelligent security, stable and efficient target tracking from diverse sensor data has become increasingly important. Self-supervised visual tracking has attracted increasing attention due to its potential to eliminate reliance on costly manual annotations; however, existing methods often train on incomplete object representations, resulting in inaccurate localization during inference. In addition, current methods typically struggle when applied to deep networks. To address these limitations, we propose a novel self-supervised tracking framework based on image synthesis and domain adversarial learning. We first construct a large-scale database of real-world target objects, then synthesize training video pairs by randomly inserting these targets into background frames while applying geometric and appearance transformations to simulate realistic variations. To reduce domain shift introduced by synthetic content, we incorporate a domain classification branch after feature extraction and adopt domain adversarial training to encourage feature alignment between real and synthetic domains. Experimental results on five standard tracking benchmarks demonstrate that our method significantly enhances tracking accuracy compared to existing self-supervised approaches without introducing any additional labeling cost. The proposed framework not only ensures complete target coverage during training but also shows strong scalability to deeper network architectures, offering a practical and effective solution for real-world tracking applications. |
| format | Article |
| id | doaj-art-67a46acd0b2e4b28a0287a592b29bd68 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-67a46acd0b2e4b28a0287a592b29bd682025-08-20T04:00:49ZengMDPI AGSensors1424-82202025-07-012515462110.3390/s25154621Self-Supervised Visual Tracking via Image Synthesis and Domain Adversarial LearningGu Geng0Sida Zhou1Jianing Tang2Xinming Zhang3Qiao Liu4Di Yuan5Guangzhou Institute of Technology, Xidian University, Guangzhou 510555, ChinaSchool of Electrical and Information Engineering, Yunnan Minzu University, Kunming 650504, ChinaSchool of Electrical and Information Engineering, Yunnan Minzu University, Kunming 650504, ChinaSchool of Science, Harbin Institute of Technology, Shenzhen 518055, ChinaNational Center for Applied Mathematics, Chongqing Normal University, Chongqing 401331, ChinaGuangzhou Institute of Technology, Xidian University, Guangzhou 510555, ChinaWith the widespread use of sensors in applications such as autonomous driving and intelligent security, stable and efficient target tracking from diverse sensor data has become increasingly important. Self-supervised visual tracking has attracted increasing attention due to its potential to eliminate reliance on costly manual annotations; however, existing methods often train on incomplete object representations, resulting in inaccurate localization during inference. In addition, current methods typically struggle when applied to deep networks. To address these limitations, we propose a novel self-supervised tracking framework based on image synthesis and domain adversarial learning. We first construct a large-scale database of real-world target objects, then synthesize training video pairs by randomly inserting these targets into background frames while applying geometric and appearance transformations to simulate realistic variations. To reduce domain shift introduced by synthetic content, we incorporate a domain classification branch after feature extraction and adopt domain adversarial training to encourage feature alignment between real and synthetic domains. Experimental results on five standard tracking benchmarks demonstrate that our method significantly enhances tracking accuracy compared to existing self-supervised approaches without introducing any additional labeling cost. The proposed framework not only ensures complete target coverage during training but also shows strong scalability to deeper network architectures, offering a practical and effective solution for real-world tracking applications.https://www.mdpi.com/1424-8220/25/15/4621object trackingself-supervisedimage synthesisdomain adversarial learning |
| spellingShingle | Gu Geng Sida Zhou Jianing Tang Xinming Zhang Qiao Liu Di Yuan Self-Supervised Visual Tracking via Image Synthesis and Domain Adversarial Learning Sensors object tracking self-supervised image synthesis domain adversarial learning |
| title | Self-Supervised Visual Tracking via Image Synthesis and Domain Adversarial Learning |
| title_full | Self-Supervised Visual Tracking via Image Synthesis and Domain Adversarial Learning |
| title_fullStr | Self-Supervised Visual Tracking via Image Synthesis and Domain Adversarial Learning |
| title_full_unstemmed | Self-Supervised Visual Tracking via Image Synthesis and Domain Adversarial Learning |
| title_short | Self-Supervised Visual Tracking via Image Synthesis and Domain Adversarial Learning |
| title_sort | self supervised visual tracking via image synthesis and domain adversarial learning |
| topic | object tracking self-supervised image synthesis domain adversarial learning |
| url | https://www.mdpi.com/1424-8220/25/15/4621 |
| work_keys_str_mv | AT gugeng selfsupervisedvisualtrackingviaimagesynthesisanddomainadversariallearning AT sidazhou selfsupervisedvisualtrackingviaimagesynthesisanddomainadversariallearning AT jianingtang selfsupervisedvisualtrackingviaimagesynthesisanddomainadversariallearning AT xinmingzhang selfsupervisedvisualtrackingviaimagesynthesisanddomainadversariallearning AT qiaoliu selfsupervisedvisualtrackingviaimagesynthesisanddomainadversariallearning AT diyuan selfsupervisedvisualtrackingviaimagesynthesisanddomainadversariallearning |